FIP 0014 thematic notebook

The goal of this notebook is to explore the impact of the upcoming sector expiration wave on April / May, especially when considering the ones with V1 proofs.

Setup and dependences

%load_ext autotime

%load_ext autoreload

%autoreload 2

time: 9.58 ms (started: 2021-08-12 15:06:49 +00:00)

# External dependences
import pandas as pd
import numpy as np
import plotly.express as px
from prophet import Prophet
import matplotlib.pyplot as plt

# Move path to parent folder
import sys
sys.path.insert(1, '../')

import plotly
plotly.offline.init_notebook_mode()

time: 886 ms (started: 2021-08-12 15:06:49 +00:00)

# Create a connection object from a conn string
from filecoin_metrics.connection import get_connection, get_connection_string
conn_string = get_connection_string('../config/sentinel-conn-string.txt')
connection = get_connection(conn_string)

time: 1.47 s (started: 2021-08-12 15:06:50 +00:00)

QUERY = """
/* Get the last state of the sectors */
with sector_states as ( 
	select 
	msi.*,
	max(msi.height) over (partition by msi.sector_id, msi.miner_id) as max_height
	from miner_sector_infos msi 
	where msi.activation_epoch > 0
	and msi.expiration_epoch > msi.height /* Get only active sectors */
	order by max_height 
)
select 
count(*) as sector_count,
sum(ss.initial_pledge::numeric) / 1e18 as initial_pledge_in_fil,
count(*) * 32 as network_power_in_gb,
date_trunc('DAY', to_timestamp(height_to_unix(ss.activation_epoch))) as activation_date,
date_trunc('DAY', to_timestamp(height_to_unix(ss.expiration_epoch))) as expiration_date
from sector_states as ss
where ss.max_height = ss.height /* get the last state of the info */
group by activation_date, expiration_date
order by activation_date, expiration_date
"""

query_df = (pd.read_sql(QUERY, connection)
              .assign(network_power_in_pib=lambda df: df.network_power_in_gb / (1024 ** 2))
              .assign(initial_pledge_in_thousand_fil=lambda df: df.initial_pledge_in_fil / 1000))

time: 4min 24s (started: 2021-08-12 15:06:52 +00:00)

# Maximum date for V1 sectors
UPGRADE_DATE = '2020-11-25 00:00:00'

metrics = {'is_v1': lambda x:  x['activation_date'] < UPGRADE_DATE}

query_df = query_df.assign(**metrics)

time: 17.3 ms (started: 2021-08-12 15:11:17 +00:00)

def resample_and_bar_plot(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig


def resample_and_bar_plot_relative(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    y = fig_df.groupby(time_column).sum()
    fig_df /= y
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig

time: 16 ms (started: 2021-08-12 15:11:17 +00:00)

Sector Count

df = query_df.copy()

print("Basic stats")
print("---")
print(f"Total sectors (#): {df.sector_count.sum()}")
print(f"Raw bytes power (PiB): {df.network_power_in_gb.sum() / (1024 ** 2) :.3g}")
print(f"Initial pledge (FIL): {df.initial_pledge_in_fil.sum()}")
print("---")

Basic stats
---
Total sectors (#): 73156149
Raw bytes power (PiB): 2.23e+03
Initial pledge (FIL): 22000392.535066683
---
time: 15.6 ms (started: 2021-08-12 15:11:17 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 337 ms (started: 2021-08-12 15:11:17 +00:00)

resample_rule = '1d'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors Before 15Jun2021 (log #)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']



fig_df = (df.query("expiration_date < '2021-06-15 00:00+00:00'")
            .groupby(groups)
            .sum()
            .reset_index()
         )

if fig_df.empty:
   print('WARNING: DataFrame is Empty')
else:
   fig = px.bar(fig_df,
               x=time_column,
               y=value_column,
               facet_row=fig_df.is_v1,
               title=title,
               log_y=True)
   fig.show()

WARNING: DataFrame is Empty
time: 27.4 ms (started: 2021-08-12 15:11:17 +00:00)

sector_count = df.sector_count.sum()
v1_count = df.query("is_v1 == True").sector_count.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .sector_count
                           .sum())

print("---")
print(f"Total sectors (#): {sector_count}")
print(f"V1 sectors (#): {v1_count}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total sectors (#): 73156149
V1 sectors (#): 1189468
V1 sectors share (%): 1.6%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 1.63%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 100.00%
---
time: 34.3 ms (started: 2021-08-12 15:11:17 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Upcoming Sector Expiration Count, grouped by sector version (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 104 ms (started: 2021-08-12 15:11:17 +00:00)

resample_rule = '1w'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Activated Sector Count, grouped by sector version (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 107 ms (started: 2021-08-12 15:11:17 +00:00)

Forecasts

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
             .assign(y=lambda df: df.y.cumsum() / (32 * 1024 * 1024)))

m = Prophet(changepoint_prior_scale=0.4)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted RB Storage Power')
plt.xlabel("Time")
plt.ylabel("Total RB Storage Power (EiB)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -35.0297
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       1086.56     0.0292297       1282.13           1           1      122   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       1183.01     0.0069328       377.733           1           1      236   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       1212.75    0.00199629       104.729      0.2338           1      352   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     399       1220.62    0.00136294       129.752           1           1      461   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     499       1233.56    0.00607426       618.034           1           1      571   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     599       1241.13     0.0105528       313.415           1           1      693   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     699       1248.06   0.000224052       45.1991      0.8022      0.8022      809   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     799       1249.22    0.00624081       154.738           1           1      929   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     899       1250.14    0.00431049       94.3784      0.6392      0.6392     1041   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     999       1250.54    0.00147831       53.8892           1           1     1163   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1075       1250.66   6.14965e-06       12.3639     3.5e-07       0.001     1296  LS failed, Hessian reset 
    1099       1250.75   0.000431175       27.7571      0.6377      0.6377     1327   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1199       1250.88   3.52055e-05       26.9327   1.571e-06       0.001     1501  LS failed, Hessian reset 
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1299       1251.11    0.00781204       244.932           1           1     1618   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1399       1251.93     0.0041213       46.4634      0.6747      0.6747     1732   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1499       1252.25   0.000283805       32.4674      0.3988      0.3988     1852   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1599       1252.37   6.42656e-05       11.6967           1           1     1972   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1699       1252.47   0.000352422       12.3669       3.992      0.3992     2092   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1799       1252.55   2.14488e-05       14.5461     0.01143           1     2223   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1899       1252.56   7.52909e-05       16.8549      0.6063      0.6063     2354   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1965       1252.78   2.09283e-05       33.9227   9.763e-07       0.001     2511  LS failed, Hessian reset 
    1999       1252.83   1.16949e-05       29.8056     0.02368           1     2552   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    2043       1252.85   7.09077e-06       14.1598   3.437e-07       0.001     2645  LS failed, Hessian reset 
    2070       1252.86   4.23508e-06        6.1592   2.568e-07       0.001     2722  LS failed, Hessian reset 
    2099       1252.86   0.000231838       13.0663           1           1     2759   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    2199       1252.88   1.07492e-05       8.20954           1           1     2893   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    2260       1252.88   1.26457e-06       7.18272      0.3482           1     2967   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 3.66 s (started: 2021-08-12 15:11:17 +00:00)

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -10.1148
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       539.511      0.061665       53.5025           1           1      125   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       548.652     0.0114822       39.2769           1           1      249   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299        549.68   0.000879443       20.8058      0.5639      0.5639      371   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     399       549.984    0.00094087       19.9121      0.5233      0.5233      505   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     420       550.436   0.000545565       39.5618   2.213e-05       0.001      563  LS failed, Hessian reset 
     499       550.653   0.000274347       14.4428           1           1      670   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     562       550.687   0.000192481       22.5034   9.966e-06       0.001      773  LS failed, Hessian reset 
     599       550.694   1.80431e-05       22.0015           1           1      817   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     617       550.695   4.16559e-06       17.1881   2.284e-07       0.001      883  LS failed, Hessian reset 
     659       550.696   5.62066e-08       17.6953      0.1049      0.1049      938   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 2.17 s (started: 2021-08-12 15:11:21 +00:00)

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00' & activation_date < '2021-03-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=90)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors without March data')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -4.476
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      72       151.528    0.00255944       21.4481   7.658e-05       0.001      120  LS failed, Hessian reset 
      99       151.713   0.000149277       17.1013       2.201      0.2201      163   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     144       151.717   0.000179243       13.4875   8.639e-06       0.001      264  LS failed, Hessian reset 
     199       151.718   9.43985e-06       17.6273           1           1      342   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     226       151.733   5.13839e-05       17.9329   3.145e-06       0.001      416  LS failed, Hessian reset 
     252       151.733   9.53697e-09       15.6199     0.06896     0.06896      461   
Optimization terminated normally: 
  Convergence detected: absolute parameter change was below tolerance

time: 2.2 s (started: 2021-08-12 15:11:23 +00:00)

rename_cols = {'activation_date': 'ds',
               0: 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .apply(lambda x: (x.initial_pledge_in_fil / x.sector_count).mean())
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
          )

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Initial Pledge per Sector')
plt.xlabel("Time")
plt.ylabel("Mean Initial Pledge per Sector (FIL)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -5.28229
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       324.683   0.000123082       17.1488       0.168       0.168      130   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       325.566   0.000132078       14.9216      0.5334      0.5334      259   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       326.159   6.83387e-05       16.8753   3.783e-06       0.001      434  LS failed, Hessian reset 
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     318       326.159   5.36507e-08       13.2791      0.2736           1      469   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 2.27 s (started: 2021-08-12 15:11:25 +00:00)

m.fit

<bound method Prophet.fit of <prophet.forecaster.Prophet object at 0x7f84ae348940>>

time: 26.1 ms (started: 2021-08-12 15:11:28 +00:00)

resample_rule = '7d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
px.density_heatmap(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count')

time: 134 ms (started: 2021-08-12 15:11:28 +00:00)

resample_rule = '1d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
fig = px.density_contour(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count',
                   histfunc='sum')
fig.show()

time: 474 ms (started: 2021-08-12 15:11:28 +00:00)

Initial Pledge

sector_count = df.initial_pledge_in_fil.sum()
v1_count = df.query("is_v1 == True").initial_pledge_in_fil.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

print("---")
print(f"Total collateral (Million FIL): {sector_count / 1e6 :.3g}")
print(f"V1 collateral (Million FIL): {v1_count / 1e6 :.3g}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total collateral (Million FIL): 22
V1 collateral (Million FIL): 0.376
V1 sectors share (%): 1.7%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 1.71%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 100.00%
---
time: 43.5 ms (started: 2021-08-12 15:11:28 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 179 ms (started: 2021-08-12 15:11:28 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 105 ms (started: 2021-08-12 15:11:29 +00:00)

resample_rule = '1m'
time_column = 'activation_date'
value_column = ['initial_pledge_in_thousand_fil']
title = 'Sum of Initial Pledge (FIL) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 98.8 ms (started: 2021-08-12 15:11:29 +00:00)

RB Network Power

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 88.4 ms (started: 2021-08-12 15:11:29 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 102 ms (started: 2021-08-12 15:11:29 +00:00)

resample_rule = '1m'
time_column = 'activation_date'
value_column = ['network_power_in_pib']
title = 'Sum of RB Network Power (PiB) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 80.8 ms (started: 2021-08-12 15:11:29 +00:00)